Optimized recurrent neural network mechanism for olive leaf disease diagnosis based on wavelet transform

Abstract

Plant diseases can significantly reduce food and agricultural production, leading to major quality, quantity, and economic losses. Plant disease deficits are usually reduced by early diagnosis through visual observation. Significant plant species grown in specific parts of the world include olives. Depending on the place where an olive tree is produced, many diseases can affect it. Traditional plant disease detection is ineffective and time-consuming. Therefore, this paper developed an in-depth evaluation of RNN architecture with an Ant Colony Optimization algorithm for disease identification in olive trees. It consists of a dataset that contains 3300 images of healthy and diseased leaves. The images are gathered in a dataset and then pro-processed. After pre-processing the images, the segmentation process is done using Wavelet transform. Then, the Ant Colony Optimization algorithm is employed for extracting the features. Lastly, categorization is done by applying RNN. The results suggest the optimal model for creating an efficient disease detector. The developed method attains higher performance when compared with other existing techniques. Hence, the outcomes demonstrate that the ACO-RNN technique has a reliable potential for identifying plant infections.

Keywords

• Olive leaf
• Deep Learning
• Ant Colony Optimization
• Recurrent Neural Network